The remote sensing and probabilistic sampling based method for determining the carbon dioxide volume of a forest relates generally to analyzing remote sensing data, such as digital images and LiDAR data, to extract, classify, and analyze aggregate and individual features, such as trees in order to produce an accurate forest inventory. More particularly, the remote sensing and probabilistic sampling based method for determining the carbon dioxide volume of a forest relates to a method for producing an accurate determination of the amount of carbon dioxide volume per acre of the forest.
The importance of calculating carbon sequestration in forestland is important for a variety of reasons. Deforestation is estimated to be responsible for 20-25% of the world's greenhouse gas emissions (IPCC 2001), including carbon dioxide (CO2). The ability to calculate baseline forested carbon stocks as well as monitor change over time with statistical precision and accuracy is critical to understanding advancements or declines in sequestration efforts in the United States and around the world.
The probabilistic sampling forest inventory method of collecting quantifiable inputs for the accurate calculation of carbon stocks can provide a significant advancement in carbon sequestration measurement and monitoring of forested carbon stocks. This advancement is important in potentially reducing the cost of data input collection as well as the ability to systematically process the inputs for calculations of potentially billions of acres with statistical precision and accuracy unmatched heretofore.